Locality Sensitive Hashing (LSH)

I have written a fairly simple Matlab toolbox, implementing two versions of LSH: the old one with binary axis-parallel stumps, and the more recent E2LSH scheme with random projections and integer, rather than binary, hash key values.The toolbox is contained in the archive lshcode.tar.gz, which includes the code and a README file.

The README walks through an example data set; the data (image
patches) as well as a few LSH structures build for it as described in
the README, are available in lshtst.tar.gz (54.6 MB; fixed 6/26/09).

You can download the code and auxiliary files in one archive: PoserPython_GS.tar.gz. The archive contains a number of directories, as described in the Readme.txt file (itself included in the archive).

In addition, you can download a sample of the data used in ICCV'05 and CVPR'06 papers (and in my PhD thesis). These files contain the total of 90,000 examples of pose, rendered in stereo, with ground truth foreground/background mask:

I have actually generated about 1,500,000 labeled images based on motion capture data, however making them all available is not possible at the moment due to space limitations on the server. With a bit of effort you can generate your own database like this using the code I provide, along with freely available motion capture sequences from mocapdata.com.

I am planning to make available the edge direction histogram feature representation for these data some time soon; for now, you can try to compute them using the code in the matlab directory included in the archive.